Speech Emotion Recognition Quiz

Answers and explanations appear as you take the quiz.

1. What does Speech Emotion Recognition primarily analyze?
   • Acoustic features of the voice like pitch and energy
   • The literal words in a transcript
   • The grammar of the sentence
   • The speaker's facial expressions
2. Which acoustic properties can speech-emotion models use as cues?
   • Only the spelling of transcribed words
   • Pitch, energy, speaking rate, and voice-quality features
   • A person’s job title
   • The number of speakers in a company
3. Why is labeling emotion data especially difficult?
   • Audio files are too large to store
   • Microphones cannot record emotion
   • Emotion is subjective and human annotators often disagree
   • Emotions never appear in speech
4. Which of these is a well-known emotional speech dataset?
   • ImageNet
   • COCO
   • SQuAD
   • IEMOCAP
5. How do modern SER systems often leverage limited labeled data?
   • By fine-tuning self-supervised models like wav2vec 2.0
   • By avoiding neural networks entirely
   • By using only handwritten rules
   • By recording millions of new emotional clips first
6. What does the EU AI Act prohibit regarding emotion recognition?
   • All emotion-related research in any setting
   • Inferring emotions in workplace or educational settings, except for medical or safety reasons
   • Every voice assistant that detects speech
   • Any system that transcribes spoken words
7. Why can a speech-emotion model trained on one dataset fail on another?
   • Emotion labels are identical across every corpus
   • Microphone and speaker differences never affect audio
   • Speakers, language, recording conditions, elicitation, and label distributions can differ
   • A trained model cannot process new recordings
8. What is one limitation of treating a speech-emotion prediction as a person’s true internal state?
   • Acoustic features contain no information
   • A predicted label is a clinical diagnosis by default
   • The same voice always expresses the same emotion
   • A model infers patterns from audio and may not reflect the speaker’s context or self-report